Early Onset Bullous Emphysema Associated with Polysubstance Use

The burden of COPD in the United States is tremendous. This disease is not only among the leading causes of mortality annually, but also takes a heavy financial toll.1 Bullous emphysema is a severe variant of COPD. The primary identified risk factor for bullous emphysema is tobacco use; however, the impact of other substances is not clearly delineated.2 This case presents a patient diagnosed with severe bullous emphysema at age 33 with substantial disease progression over the course of 12 years associated with much scarcer tobacco use than would be expected but a prominent history of methamphetamine and marijuana use. Marijuana and amphetamine-type stimulants are the most widely used illicit substances in the world, and prevalence of both are increasing in the United States. In 2020, an estimated 14.2 million Americans had a marijuana use disorder and 1.5 million had a methamphetamine use disorder.3-7 A better understanding of how these substances may contribute to development and progression of chronic lung disease, both individually and perhaps synergistically, is necessary to guide discussions with patients and inform effective public health efforts

CNSEG-GAN: a lightweight generative adversarial network for segmentation of CRL and NT from first-trimester fetal ultrasound

This paper presents a novel, low-compute and efficient generative adversarial network (GAN) design for automatic segmentation called CNSeg-GAN, which combines 1-D kernel factorized networks, spatial and channel attention, and multi-scale aggregation mechanisms in a conditional GAN (cGAN) fashion. The proposed CNSeg-GAN architecture is trained and tested on a first-trimester ultrasound (US) scan video dataset for automatic detection and segmentation of anatomical structures in the midsagittal plane to enable Crown Rump Length (CRL) and Nuchal Translucency (NT) measurement. Experimental results shows that the proposed CNSeg-GAN is x15 faster than U-Net and yields mIoU of 78.20% on the CRL and 89.03% on the NT dataset, respectively with only 2.19 millions in parameters. The accuracy of this lightweight design makes it well-suited for real-time deployment in future work

Towards standard plane prediction of fetal head ultrasound with domain adaption

Fetal Standard Plane (SP) acquisition is a key step in ultrasound based assessment of fetal health. The task detects an ultrasound (US) image with predefined anatomy. However, it requires skill to acquire a good SP in practice, and trainees and occasional users of ultrasound devices can find this challenging. In this work, we consider the task of automatically predicting the fetal head SP from the video approaching the SP. We adopt a domain transfer learning approach that maps the encoded spatial and temporal features of video in the source domain to the spatial representations of the desired SP image in the target domain, together with adversarial training to preserve the quality of the resulting image. Experimental results show that the predicted head plane is plausible and consistent with the anatomical features expected in a real SP. The proposed approach is motivated to support non-experts to find and analyse a trans-ventricular (TV) plane but could also be generalized to other planes, trimesters, and ultrasound imaging tasks for which standard planes are defined

Respiratory tract infection and risk of bleeding in oral anticoagulant users: self-controlled case series

Objective To estimate the association between untreated, community acquired, respiratory tract infections and bleeding in oral anticoagulant users. Design Self-controlled case series. Setting General practices in England contributing data to the Clinical Practice Research Datalink GOLD. Participants 1208 adult users of warfarin or direct oral anticoagulants with a general practice or hospital admission record of a bleeding event between January 2010 and December 2019, and a general practice record of a consultation for a community acquired respiratory tract infection for which immediate antibiotics were not prescribed (that is, untreated). Main outcome measures Relative incidence of major bleeding and clinically relevant non-major bleeding in the 0-14 days after an untreated respiratory tract infection, compared to unexposed time periods. Results Of 1208 study participants, 58% (n=701) were male, median age at time of first bleed was 79 years (interquartile range 72-85), with a median observation period of 2.4 years (interquartile range 1.3-3.8). 292 major bleeds occurred during unexposed time periods and 41 in the 0-14 days after consultation for a respiratory tract infection. 1003 clinically relevant non-major bleeds occurred during unexposed time periods and 81 in the 0-14 days after consultation for a respiratory tract infection. After adjustment for age, season, and calendar year, the relative incidence of major bleeding (incidence rate ratio 2.68, 95% confidence interval 1.83 to 3.93) and clinically relevant non-major bleeding (2.32, 1.82 to 2.94) increased in the 0-14 days after an untreated respiratory tract infection. Findings were robust to several sensitivity analyses and did not differ by sex or type of oral anticoagulant. Conclusions This study observed a greater than twofold increase in the risk of bleeding during the 0-14 days after an untreated respiratory tract infection. These findings have potential implications for how patients and clinicians manage oral anticoagulant use during an acute intercurrent illness and warrant further investigation into the potential risks and how they might be mitigated

Automating the human action of first-trimester biometry measurement from real-world freehand ultrasound

Objective: Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution represents only part of a human task, which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in freehand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three subactions. Methods: In this article, we consider how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid convolutional neural network (CNN) architecture design, a classification regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes that contain the mid-sagittal view of the fetus are sampled at multiple time stamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown–rump length (CRL) estimate is calculated as the mean CRL from these multiple frames. Results: Our fully automated method has a high correlation with clinical expert CRL measurement (Pearson's p = 0.92, R-squared [R2] = 0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos. Conclusion: A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards, ensuring precise biometric measurements

Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans

This paper presents a novel, fully-automatic framework for fetal echocardiography analysis of full-length routine firsttrimester fetal ultrasound scan video. In this study, a new deep learning architecture, which considers spatio-temporal information and spatial attention, is designed to temporally partition ultrasound video into semantically meaningful segments. The resulting automated semantic annotation is used to analyse cardiac examination workflow. The proposed 2D+t convolution neural network architecture achieves an A1 accuracy of 96.37%, F1 of 95.61%, and precision of 96.18% with 21.49% fewer parameters than the smallest ResNet-based architecture. Automated deep-learning based semantic annotation of unlabelled video scans (n=250) shows a high correlation with expert cardiac annotations (ρ = 0.96, p = 0.0004), thereby demonstrating the applicability of the proposed annotation model for echocardiography workflow analysis